Summarise multiple values to a single value.

.data | tbl A |
---|---|

... | Name-value pairs of summarizing expressions, see details |

.groups | Defaults to "drop_last" in srvyr meaning that the last group is peeled off, but if there are more groups they will be preserved. Other options are "drop", which drops all groups, "keep" which keeps all of them and "rowwise" which converts the object to a rowwise object (meaning calculations will be performed on each row). |

.unpack | Whether to "unpack" named |

Summarise for `tbl_svy`

objects accepts several specialized functions.
Each of the functions a variable (or two, in the case of
`survey_ratio`

), from the data.frame and default to providing the measure
and its standard error.

The argument `vartype`

can choose one or more measures of uncertainty,
`se`

for standard error, `ci`

for confidence interval, `var`

for variance, and `cv`

for coefficient of variation. `level`

specifies the level for the confidence interval.

The other arguments correspond to the analogous function arguments from the survey package.

The available functions from srvyr are:

`survey_mean`

Calculate the mean of a numeric variable or the proportion falling into

`groups`

for the entire population or by`groups`

. Based on`svymean`

and`svyciprop`

. .
`survey_total`

Calculate the survey total of the entire population or by

`groups`

. Based on`svytotal`

.`survey_prop`

Calculate the proportion of the entire population or by

`groups`

. Based on`svyciprop`

.`survey_ratio`

Calculate the ratio of 2 variables in the entire population or by

`groups`

. Based on`svyratio`

.`survey_quantile`

&`survey_median`

Calculate quantiles in the entire population or by

`groups`

. Based on`svyquantile`

.`unweighted`

Calculate an unweighted estimate as you would on a regular

`tbl_df`

. Based on dplyr's`summarise`

.

You can use expressions both in the `...`

of `summarize`

and also
in the arguments to the summarizing functions. Though this is valid syntactically
it can also allow you to calculate incorrect results (for example if you multiply
the mean by 100, the standard error is also multipled by 100, but the variance
is not).

data(api, package = "survey") dstrata <- apistrat %>% as_survey_design(strata = stype, weights = pw) dstrata %>% summarise(api99_mn = survey_mean(api99), api00_mn = survey_mean(api00), api_diff = survey_mean(api00 - api99))#> # A tibble: 1 × 6 #> api99_mn api99_mn_se api00_mn api00_mn_se api_diff api_diff_se #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 629. 10.1 662. 9.54 32.9 2.08dstrata_grp <- dstrata %>% group_by(stype) dstrata_grp %>% summarise(api99_mn = survey_mean(api99), api00_mn = survey_mean(api00), api_diff = survey_mean(api00 - api99))#> # A tibble: 3 × 7 #> stype api99_mn api99_mn_se api00_mn api00_mn_se api_diff api_diff_se #> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 E 636. 13.3 674. 12.5 38.6 2.76 #> 2 H 617. 15.8 626. 15.5 8.46 3.41 #> 3 M 610. 16.8 637. 16.6 26.4 3.05# `dplyr::across` can be used to programmatically summarize multiple columns # See https://dplyr.tidyverse.org/articles/colwise.html for details # A basic example of working on 2 columns at once and then calculating the total # the mean total_vars <- c("enroll", "api.stu") dstrata %>% summarize(across(c(all_of(total_vars)), survey_total))#> # A tibble: 1 × 4 #> enroll enroll_se api.stu api.stu_se #> <dbl> <dbl> <dbl> <dbl> #> 1 3687178. 117319. 3086009. 101841.# Expressions are allowed in summarize arguments & inside functions # Here we can calculate binary variable on the fly and also multiply by 100 to # get percentages dstrata %>% summarize(api99_over_700_pct = 100 * survey_mean(api99 > 700))#> # A tibble: 1 × 2 #> api99_over_700_pct api99_over_700_pct_se #> <dbl> <dbl> #> 1 30.6 3.61# But be careful, the variance doesn't scale the same way, so this is wrong! dstrata %>% summarize(api99_over_700_pct = 100 * survey_mean(api99 > 700, vartype = "var"))#> # A tibble: 1 × 2 #> api99_over_700_pct api99_over_700_pct_var #> <dbl> <dbl> #> 1 30.6 0.130# Wrong variance!